Evidential Object Association Using Heterogeneous Sensor Data
Résumé
Multiple Object Association is a considerable and challenging process in highly cluttered environments. Its aim is to thoroughly relate known objects to new detected ones which is hard in such conditions. The recurrent occlusions and pose variation of objects can raise ambiguity in decision-making. The objective of this paper is to establish a multi-feature fusion approach based on two sources describing, distinctly, the position and the motion direction of considered objects. The proposition is based on Dempster-Shafer theory to model the uncertainty and unreliability of sources and to ensure an evidential combination. Experimental results with real data from the KITTI database are presented to evaluate the proposed solution.